
The first step in collecting the desired large high-quality radiance field dataset is to collect a synthetic posed multi-view dataset. We use ShapeNet Core 55 [1]} as the data of choice for 3D shapes. For rendering, we used Pyglet [2]} API through Trimesh library [3]}. The renderer is based on OpenGL [4]} rasterizer to render over 17 million images of around 40,000 shapes from 13 different classes at a high resolution of \(400 \times 400\) . Every shape is rendered equidistantly from 400 views distributed in a spherical configuration surrounding the object, including from the bottom (see Figure REF  for an example). An additional 20 views are rendered from random views from the same distance as test views for novel view synthesis tasks. Furthermore, an additional 10 views are rendered randomly from random distances bounded by a reasonable range, such that at least a part of the object is guaranteed to be visible. This last set is aimed at robustness purposes to test whether novel view synthesis methods can generalize to out-of-distribution posed views. More details about the rendering setup, including lighting and materials are provided in supplementary material.
